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The difference between a small and large Gaussian blur. In image processing, a Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function (named after mathematician and scientist Carl Friedrich Gauss). It is a widely used effect in graphics software, typically to reduce image noise and reduce detail.
Adobe Photoshop implements a bilateral filter in its surface blur tool. GIMP implements a bilateral filter in its Filters → Blur tools; and it is called Selective Gaussian Blur. The free G'MIC plugin Repair → Smooth [bilateral] for GIMP adds more control. [7]
The same differencing principle is used in the unsharp-masking tool in many digital-imaging software packages, such as Adobe Photoshop and GIMP. [1] The software applies a Gaussian blur to a copy of the original image and then compares it to the original. If the difference is greater than a user-specified threshold setting, the images are (in ...
obtained by subtracting the higher-variance Gaussian from the lower-variance Gaussian. The difference of Gaussian operator is the convolutional operator associated with this kernel function. So given an n -dimensional grayscale image I : R n → R {\\displaystyle I:\\mathbb {R} ^{n}\\rightarrow \\mathbb {R} } , the difference of Gaussians of ...
Gaussian blur 5 × 5 (approximation) [] Unsharp masking 5 × 5 Based on Gaussian blur with amount as 1 and threshold as 0 (with no image mask) [ ] The above are just ...
An example of an image blurred using a box blur. A box blur (also known as a box linear filter) is a spatial domain linear filter in which each pixel in the resulting image has a value equal to the average value of its neighboring pixels in the input image. It is a form of low-pass ("blurring") filter.
Smoothing filters tend to blur an image because pixel intensity values that are significantly higher or lower than the surrounding neighborhood smear across the area. Because of this blurring, linear filters are seldom used in practice for noise reduction; [ citation needed ] they are, however, often used as the basis for nonlinear noise ...
The focal element receives the heaviest weight (having the highest Gaussian value), and neighboring elements receive smaller weights as their distance to the focal element increases. In Image processing, each element in the matrix represents a pixel attribute such as brightness or color intensity, and the overall effect is called Gaussian blur.